• Journal of Korea Water Resources Association
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• v.53 no.3
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• pp.225-236
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• 2020

It is not easy to provide sufficient lead time for flood forecast in urban and small mountain basins using on-ground rain gauges, because the time concentration in those basins is too short. In urban and small mountain basins with a short lag-time between precipitation and following flood events, it is more important to secure forecast lead times by predicting rainfall amounts. The Han River Flood Control Office (HRFCO) in South Korea produces short-term rainfall forecasts using the Mcgill Algorithm for Precipitation-nowcast by Lagrangian Extrapolation (MAPLE) algorithm that converts radar reflectance of rainfall events. The Flash Flood Research Center (FFRC) in the Korea Institute of Civil Engineering and Building Technology (KICT) installed a flash flood forecasting system using the short-term rainfall forecast data produced by the HRFCO and has provided flash flood information in a local lvel with 1-hour lead time since 2019. In this study, we addressed the flash flood forecasting system based on the radar rainfall and the assessed the accuracy of the forecasting system for the recorded flood events occurred in 2019. A total of 31 flood disaster cases were used to evaluate the accuracy and the forecast accuracy was 90.3% based on the probability of detection.

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.5
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• pp.81-89
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• 1995

One of the most difficult problem to estimate the flood inflow is how to understand the effective rainfall. The effective rainfall is absolutely influenced by the condition of soil moisture in the watershed just before the storm event. DAWAST model developed to simulate the daily streamflow considering the meteologic and geographic characteristics in the Korean watersheds was applied to understand the soil moisture and estimate the effective rainfall rather accurately through the daily water balance in the watershed. From this soil moisture and effective rainfall, concentration time, dimensionless hydrograph, and addition of baseflow, the rainfall-runoff model for flood flow was developed by converting the concept of long-term runoff into short-term runoff. And, real-time flood forecasting model was also developed to forecast the flood-inflow hydrograph to the river and reservoir, and called RETFLO model. According to the model verification, RETFLO model can be practically applied to the medium and small river and reservoir to forecast the flood hydrograph with peak discharge, peak time, and volume. Consequently, flood forecasting and warning system in the river and the reservoir can be greatly improved by using personal computer.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.107-113
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• 2019

The flood forecasting model currently used in Korea calculates the runoff of basin using the lumped rainfall-runoff model and estimates the river level using the river and reservoir routing models. The lumped model assumes homogeneous drainage zones in the basin. Therefore, it can not consider various spatial characteristics in the basin. In addition, the rainfall data used in lumped model also has the same limitation because of using the point scale rainfall data. To overcome the limitations as mentioned above, many researchers have studied to apply the distributed rainfall-runoff model to flood forecasting system. In this study, to apply the Grid-based Rainfall-Runoff Model (GRM) to the Korean flood forecasting system, the optimal resolution is determined by analyzing the difference of the results of the runoff according to the various resolutions. If the grid size is to small, the computation time becomes excessive and it is not suitable for applying to the flood forecasting model. Even if the grid size is too large, it does not fit the purpose of analyzing the spatial distribution by applying the distributed model. As a result of this study, the optimal resolution which satisfies the accuracy of the bsin runoff prediction and the calculation speed suitable for the flood forecasting was proposed. The accuracy of the runoff prediction was analyzed by comparing the Nash-Sutcliffe model efficiency coefficient (NSE). The optimal resolution estimated from this study will be used as basic data for applying the distributed rainfall-runoff model to the flood forecasting system.

• Water Engineering Research
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• v.3 no.2
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• pp.113-122
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict flash floods. In this study, a Quantitative Flood Forecasting (QFF) model was developed by incorporating the evolving structure and frequency of intense weather systems and by using neural network approach. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as lifetime, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. All these processes stretched leadtime up to 18 hours. The QFF model will be applied to the mid-Atlantic region of United States in a forthcoming paper.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1145-1149
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• 2006

The typhoon Rusa passed through the Korean peninsula from the west-southern part to the east-northern part in the summer season of 2002. The flash flood due to the Rusa was occurred over the Korean peninsula and especially the damage was concentrated in Kangnung, Yangyang, Kosung, and Jeongsun areas of Kangwon-Do. Since the latter half of the 1990s the flash flood has became one of the frequently occurred natural disasters in Korea. Flash floods are a significant threat to lives and properties. The government has prepared against the flood disaster with the structural and nonstructural measures such as dams, levees, and flood forecasting systems. However, since the flood forecasting system requires the rainfall observations as the input data of a rainfall-runoff model, it is not a realistic system for the flash flood which is occurred in the small basins with the short travel time of flood flow. Therefore, the flash flood forecasting system should be constructed for providing the realistic alternative plan for the flash flood. To do so, firstly, Flash Flood Monitoring and Prediction (FFMP) Model must be developed suitable to Korea terrain. In this paper, We develop the FFMP model which is based on GIS, Radar techniques and hydro-geomorphologic approaches. We call it the F2MAP model. F2MAP model has three main components (1) radar rainfall estimation module for the Quantitative Precipitation Forecasts (QPF), (2) GIS Module for the Digital terrain analysis, called TOPAZ(Topographic PArametiZation), (3) hydrological module for the estimation of threshold runoff and Flash Flood Guidance(FFG). For the performance test of the model developed in this paper, F2MAP model applied to the Kangwon-Do, Korea, where had a severe damage by the Typhoon Rusa in August, 2002. The result shown that F2MAP model is suitable for the monitoring and the prediction of flash flood.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.285-288
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• 2002

For real-time flood forecasting and effective control flood at the Youngsan estuary dam, the Flood Forecasting and Control User Interface System II (FFCUS II) has been developed. This paper describes the features and application of FFCUS II. FFCUS II is composed of the database management subsystem, the model subsystem, and the graphic user interface. The database management subsyem collects rainfall data and stream flow data, updates, processes, and searches the data. The model subsystem predicts the inflow hydrograph, the tide, forecasts flood hydrograph, and simulates the release rate from the sluice gates. The graphic user interface subsystem aids the user's decision-making process by displaying the operation results of the database management subsystem and model subsystem.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.51-65
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• 2010

We suggest a simple and practical flood forecasting and warning system, which can predict change in the water level of a river in a small to medium-size watershed where flash flooding occurs in a short time. We first choose the flood defense target points, through evaluation of the flood risk of dike overflow and lowland inundation. Using data on rainfall, and on the water levels at the observed and prediction points, we investigate the interrelations and derive a regression formula from which we can predict the flood level at the target points. We calculate flood water levels through a calibrated flood simulation model for various rainfall scenarios, to overcome the shortage of real water stage data, and these results as basic population data are used to derive a regression formula. The values calculated from the regression formula are modified by the weather condition factor, and the system can finally predict the flood stages at the target points for every leading time. We also investigate the applicability of the prediction procedure for real flood events of the Jungnang Stream basin, and find the forecasting values to have close agreement with the surveyed data. We therefore expect that this suggested warning scheme could contribute usefully to the setting up of a flood forecasting and warning system for a small to medium-size river basin.

• Water for future
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• v.27 no.3
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• pp.55-69
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• 1994

In order to enhance the short-term flood forecasting accuracy of the water level of the In-do Brdg., three statistical flood forecasting models are presented models are presented and the forecasting accuracies and stabilities of the models are studied. The presented statistical models are as follows: The multi-input model by the multi-regression analysis between the water level of the In-do Brdg. and the influence parameters(Model MM). The two-level multi parameter model according to the water level tendency(Model 2MP). Among the three models, the Model MM showed the lowest forecasting accuracy, the model 2MP showed the highest forecasting accuracy, although this model sometimes became unstable and diverged. The model MMP forecasted the flood less accurately than model 2MP, but it gave more stable forecasting results.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.3
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• pp.157-164
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• 2008

A modeling and visualization system of flood inundation in natural river, FloodViz, has been developed. Unsteady river flow and flood inundation are calculated by FLDWAV model. FLDWAV model and HEC-RAS model have been applied to a flood event at the same time to check model reliability. Simulation results of the two models showed good agreements. Flood propagation and inundation process can be analyzed accurately and easily by using visualization function of the FloodViz. Even though FloodViz users don't know well about both hydraulics and hydrology, they can understand flood inundation phenomena easily. This system can be used as a useful tool in forecasting flood inundation and observing the simulation results. Countermeasures for natural disaster prevention due to flood inundation can be established rapidly by using the FloodViz.